class_docstr: Adapt pandas transformations to sktime interface.
  
  In `transform`, executes `pd.DataFrame` method of name `method` on data,
  optionally with keywords arguments passed, via `kwargs` hyper-parameter.
  The `apply_to` parameter controls what the data is upon which `method` is called:
  "call" = for `X` seen in `transform`, "all"/"all_subset" = all data seen so far.
  See below for details.
  
  For hierarchical series, operation is applied by instance.
  
  Parameters
  ----------
  method : str, optional, default = None = identity transform
      name of the method of DataFrame that is applied in transform
  kwargs : dict, optional, default = empty dict (no kwargs passed to method)
      arguments passed to DataFrame.method
  apply_to : str, one of "call", "all", "all_subset", optional, default = "call"
      "call" = method is applied to `X` seen in transform only
      "all" = method is applied to all `X` seen in `fit`, `update`, `transform`
          more precisely, the application to `self._X` is returned
      "all_subset" = method is applied to all `X` like for "all" value,
          but before returning, result is sub-set to indices of `X` in `transform`
      in "all", "all_subset", `X` seen in `transform` do not update `self._X`
  
  Examples
  --------
  >>> from sktime.transformations.series.adapt import PandasTransformAdaptor
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  
  >>> transformer = PandasTransformAdaptor("diff")
  >>> y_hat = transformer.fit_transform(y)
  
  >>> transformer = PandasTransformAdaptor("diff", apply_to="all_subset")
  >>> y_hat = transformer.fit(y.iloc[:12])
  >>> y_hat = transformer.transform(y.iloc[12:])
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code: def __init__(self, method, kwargs=None, apply_to="call"):
      self.method = method
      self.kwargs = kwargs
      self.apply_to = apply_to
  
      if not isinstance(apply_to, str):
          raise TypeError(
              f"apply_to parameter must be a str, but found {type(apply_to)}"
          )
      if apply_to not in ["call", "all", "all_subset"]:
          raise ValueError(
              'apply_to must be one of "call", "all", "all_subset", '
              f'but found "{apply_to}"'
          )
  
      super().__init__()
  
      if apply_to in ["all", "all_subset"]:
          self.set_tags(**{"remember_data": True})
  
      if apply_to == "all_subset":
          self.set_tags(**{"transform-returns-same-time-index": True})
  
      if apply_to == "call":
          self.set_tags(**{"fit_is_empty": True})
  
file_path: sktime/sktime/transformations/series/adapt.py
imports: from inspect import signature
  import numpy
  from sklearn.base import clone
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new PandasTransformAdaptor object from inherited base classes: BaseTransformer with example: obj = PandasTransformAdaptor(method, kwargs, apply_to)